Nitrogen dynamics and nitrogen‐to‐phosphorus stoichiometry in cold region agricultural streams
Bibliographic record
Abstract
Abstract Cold agricultural regions are getting warmer and experiencing shifts in precipitation patterns, which affect hydrological transport of nutrients through reduced snowpack and higher annual proportions of summer rainfall. Previous work has demonstrated that the timing of phosphorus (P) concentrations is regionally coherent in streams of the northern Great Plains, suggesting a common climatic driver. There has been less investigation into patterns of stream nitrogen (N), despite its importance for water quality. Using high‐frequency water quality data collected over 6 yr from three southern Manitoba agricultural streams, the goal of this research was to investigate seasonal patterns in N and P concentrations and the resultant impacts of these patterns on N/P stoichiometry. In the spring, high concentrations of inorganic N were associated with snowmelt runoff, while summer N was dominated by organic forms; inorganic N concentrations remained consistently low in the summer, suggesting increased biological N transformation and N removal. Relationships between N concentration and discharge showed generally weak model fits (r2 values for significant relationships ranging from .33 to .48), and the strength and direction of model fits differed among streams, seasons, and forms of N. Dissolved organic N concentrations were strongly associated with dissolved organic carbon. Nitrogen‐to‐phosphorus ratios varied among streams but were significantly lower during summer storm events (p < .0001). These results suggest that climate‐driven shifts in temperature and precipitation may negatively affect downstream water quality in this region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".